Article
Computer Science, Information Systems
Yulong Cai, Siheng Mi, Jiahao Yan, Hong Peng, Xiaohui Luo, Qian Yang, Jun Wang
Summary: This paper proposes a color image segmentation method based on dynamic threshold neural networks, which simulate the spiking and dynamic threshold mechanisms of biological neurons. The method achieves regional growth and seed selection through the spiking mechanism and dynamic threshold, and controls the growth with local weights. Experimental results demonstrate the effectiveness of the proposed method in color image segmentation.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Mehran Dalvand, Abdolhossein Fathi, Arezoo Kamran
Summary: The study introduces a new model of the RG algorithm based on tissue-like P system to address the high computational complexity and low performance issues of the conventional RG algorithm. Through the utilization of GPU and CUDA programming language, the proposed model achieves better performance with a speed-up of about 12.5x. Qualitative and quantitative evaluations demonstrate that the proposed method not only maintains overall segmentation accuracy but also performs better on images with complicated backgrounds.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Computer Science, Interdisciplinary Applications
Nick Byrne, James R. Clough, Israel Valverde, Giovanni Montana, Andrew P. King
Summary: This study proposes a CNN-based multi-class segmentation method that improves the topological structure of the segmentation by capturing global anatomical features. The authors also provide an efficient implementation method and conduct detailed experiments on publicly available datasets.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Information Systems
Suxia Jiang, Yijun Liu, Bowen Xu, Junwei Sun, Yanfeng Wang
Summary: In this study, asynchronous numerical spiking neural (ANSN) P systems are investigated by combining set theory and threshold control strategy. It is proved that ANSN P systems are Turing universal and capable of processing information.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Zhang Sun, Luis Valencia -Cabrera, Guimin Ning, Xiaoxiao Song
Summary: Spiking neural P systems are an abstraction of the structure and function of nervous systems and neurons. SNP-WOD systems, a new class of these systems, remove the mechanism of duplication and allow for the amplification of pulses during the firing of spiking rules. These systems have computational properties and can generate numbers.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaoxiao Song, Luis Valencia-Cabrera, Hong Peng, Jun Wang
Summary: This paper introduces a new neural computing model - spiking neural P systems with autapses (SNP-AU systems) and demonstrates their ability to generate Turing-computable numbers. By building an SNP-AU system with 53 neurons and providing a universal machine, the universality of its computing function is shown.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Aocheng Li, Jie Guo, Yanwen Guo
Summary: A new image stitching method is proposed in this paper, which aligns a set of matched dominant semantic planar regions to improve the precision and accuracy of image stitching, utilizing semantic information and deep Convolutional Neural Network.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Theory & Methods
Jie Xue, Liwen Ren, Bosheng Song, Yujie Guo, Jie Lu, Xiyu Liu, Guanzhong Gong, Dengwang Li
Summary: We propose a hypergraph-based numerical neural-like (HNN) P system, which contains five types of neurons to capture the high-order correlations among neuron structures. Additionally, three new communication mechanisms are introduced to handle numerical variables and functions. Experimental results on medical image segmentation demonstrate that the proposed HNN P system outperforms existing methods, showing its effectiveness in accurately segmenting tumors/organs.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yanda Meng, Hongrun Zhang, Yitian Zhao, Xiaoyun Yang, Yihong Qiao, Ian J. C. MacCormick, Xiaowei Huang, Yalin Zheng
Summary: This study introduces a novel deep learning framework based on graph neural networks, with multiple graph reasoning modules to explicitly incorporate region and boundary features, along with iterative message aggregation and node update mechanism. By utilizing multi-level feature node embeddings in different parallel graph reasoning modules, the model can concurrently address region and boundary feature reasoning and aggregation at various feature levels.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Information Systems
Bosheng Song, Xiangxiang Zeng, Alfonso Rodriguez-Paton
Summary: This paper introduces monodirectional tissue P systems with channel states, where communication only occurs in one direction between two specified regions; it is proved that the system is universal by combining different numbers of cells, states, and maximum lengths for symport rules; computational efficiency of the system is further analyzed with cell division rules incorporated, and a solution to the Boolean satisfiability problem is provided using a specific maximum length for symport rules.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Mehran Dalvand, Abdolhossein Fathi, Arezoo Kamran
Summary: Interactive image segmentation is a method that uses user input to accurately segment objects from the background. Current techniques are sensitive to the location and number of seed points, requiring users to repeat the process multiple times. This paper proposes a parallel fusion model using majority voting technique, which is more reliable and requires less user interaction. Evaluation and comparison with state-of-the-art methods demonstrate the efficiency of the proposed model.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shengxin Zha, Daizong Tian, Thrasyvoulos N. Pappas
Summary: This study presents a pattern-based approach for image reconstruction using statistics and human segmentation. Experimental results demonstrate its superior performance in terms of reconstruction error rate and perceptual quality.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Rogelio V. Gungon, Katreen Kyle M. Hernandez, Francis George C. Cabarle, Ren Tristan A. De la Cruz, Henry N. Adorna, Miguel A. Martinez-del-Amor, David Orellana-Martin, Ignacio Perez-Hurtado
Summary: This study presents a parallel framework for the evolution of spiking neural P systems, implemented on a CUDA-enabled graphics processing unit. The experimental results show that the GPU-based evolution is 9 times faster than the CPU-based evolution, and the overall GPU framework is 3 times faster than the CPU version.
Article
Chemistry, Multidisciplinary
Xiong Chen, Ping Guo
Summary: This paper studies four basic arithmetic operations and improves the parallelization of addition and multiplication methods. It designs more effective SNPS for natural number addition, multiplication, subtraction, and division based on multiple subtractions. The proposed SNPS is verified to be effective through examples. Compared with similar SNPS, our system reduces the number of neurons used and the time overhead for addition operation by 50% and 33% respectively, and reduces the number of neurons used for multiplication operation by 40%.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Smaine Mazouzi, Zahia Guessoum
Summary: The paper presents a method for fast image processing using distributed and parallel computing techniques applied at the low and medium level of a vision system. The method involves a multi-agent system cooperating to produce region-based image segmentation, which results in fast and accurate segmentation.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
A. Hepzibah Christinal, Daniel Diaz-Pernil, T. Mathu
Article
Computer Science, Information Systems
Daniel Diaz-Pernil, Hepzibah A. Christinal, Miguel A. Gutierrez-Naranjo
INFORMATION SCIENCES
(2018)
Article
Computer Science, Artificial Intelligence
Daniel Diaz-Pernil, Miguel A. Gutierrez-Naranjo
Article
Computer Science, Artificial Intelligence
Fernando Diaz-del-Rio, Pablo Sanchez-Cuevas, Helena Molina-Abril, Pedro Real
PATTERN RECOGNITION LETTERS
(2020)
Article
Computer Science, Artificial Intelligence
Helena Molina-Abril, Pedro Real, Fernando Diaz-del-Rio
PATTERN RECOGNITION LETTERS
(2020)
Article
Medicine, General & Internal
Darian M. Onchis, Codruta Istin, Cristina Tudoran, Mariana Tudoran, Pedro Real
Article
Materials Science, Multidisciplinary
Guillermo Barcena-Gonzalez, Maria de La Paz Guerrero-Lebrero, Elisa Guerrero, Andres Yanez, Bernardo Nunez-Moraleda, Daniel Fernandez-Reyes, Pedro Real, David Gonzalez, Pedro L. Galindo
MICROSCOPY AND MICROANALYSIS
(2020)
Editorial Material
Computer Science, Artificial Intelligence
A. Hepzibah Christinal, Fernando Diaz-del-Rio, Rebeca Marfil, Helena Molina Abril, Darian Onchis Moaca, Pedro Real
PATTERN RECOGNITION LETTERS
(2020)
Review
Radiology, Nuclear Medicine & Medical Imaging
R. Hephzibah, Hepzibah Christinal Anandharaj, G. Kowsalya, R. Jayanthi, D. Abraham Chandy
Summary: This paper presents a comprehensive review of restoration and segmentation tasks in the medical field using deep learning techniques. The paper highlights the importance of these tasks, as restoration removes noise and segmentation extracts the specific regions of interest, both of which are crucial for accurate diagnosis and therapy. Various convolutional neural network architectures are explored, and promising results are achieved in terms of image quality improvement, noise and artifact suppression, as well as accurate segmentation of cell contours and brain tumors.
CURRENT MEDICAL IMAGING
(2023)
Article
Computer Science, Theory & Methods
Y. Preethi Ceon, Hepzibah Christinal Anandharaj, S. Jebasingh, D. Abraham Chandy
Summary: Spiking neural P systems (SN P Systems) are computational models inspired by neural biological systems, processing information through spikes and neurons. This paper defines a cell-like SN P system with multiple types of spikes and a homomorphism between the string languages generated by the system and chain code alphabets, describing the chain code picture language and cycle picture language.
JOURNAL OF MEMBRANE COMPUTING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Darian M. Onchis, Codruta Istin, Pedro Real
COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT I
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Pedro Real, Helena Molina-Abril, Fernando Diaz-del-Rio, Sergio Blanco-Trejo
COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT I
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
P. Real, H. Molina-Abril, F. Diaz-del-Rio, S. Blanco-Trejo, D. Onchis
PATTERN RECOGNITION, MCPR 2019
(2019)
Article
Mathematics, Applied
S. Blanco-Trejo, C. Aleman-Morillo, F. Diaz-del-Rio, P. Real
MATHEMATICS IN COMPUTER SCIENCE
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Darian M. Onchis, Simone Zappala, Pedro Real, Codruta Istin
2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
(2018)
